scholarly journals Choice Behavior Analysis for Feeder Modes on First and Last Mile with Qualitative Factors

2021 ◽  
Vol 39 (1) ◽  
pp. 14-29
Author(s):  
Jihye KIM ◽  
Ikki KIM ◽  
Hansol YOO
CICTP 2020 ◽  
2020 ◽  
Author(s):  
Zuyao Zhang ◽  
Li Tang ◽  
Yifeng Wang ◽  
Xuejun Zhang

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Aihua Fan ◽  
Xumei Chen ◽  
Tao Wan

In recent years, there has been rapid development in bicycle-sharing systems (BSS) in China. Moreover, such schemes are considered promising solutions to the first/last mile problem. This study investigates the mode choice behaviors of travelers for first/last mile trips before and after the introduction of bicycle-sharing systems. Travel choice models for first/last mile trips are determined using a multinomial logit model. It also analyzes the differences in choice behavior between the young and other age groups. The findings show that shared bicycles become the preferred mode, while travelers preferred walking before bicycle-sharing systems were implemented. Gender, bicycle availability, and travel frequency were the most significant factors before the implementation of bicycle-sharing systems. However, after implementation, access distance dramatically affects mode choices for first/last mile trips. When shared bicycles are available, the mode choices of middle-aged group depend mainly on gender and access distance. All factors are not significant for the young and aged groups. More than 80% of public transport travelers take walking and shared bicycles as feeder modes. The proposed models and findings contribute to a better understanding of travelers’ choice behaviors and to the development of solutions for the first/last mile problem.


2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yajuan Deng ◽  
Meiye Li ◽  
Qing Tang ◽  
Renjie He ◽  
Xianbiao Hu

Most early research on route choice behavior analysis relied on the data collected from the stated preference survey or through small-scale experiments. This manuscript focused on the understanding of commuters’ route choice behavior based on the massive amount of trajectory data collected from occupied taxicabs. The underlying assumption was that travel behavior of occupied taxi drivers can be considered as no different than the well-experienced commuters. To this end, the DBSCAN algorithm and Akaike information criterion (AIC) were first used to classify trips into different categories based on the trip length. Next, a total of 9 explanatory variables were defined to describe the route choice behavior, and and the path size (PS) logit model was then built, which avoided the invalid assumption of independence of irrelevant alternatives (IIA) in the commonly seen multinomial logit (MNL) model. The taxi trajectory data from over 11,000 taxicabs in Xi’an, China, with 40 million trajectory records each day were used in the case study. The results confirmed that commuters’ route choice behavior are heterogenous for trips with varying distances and that considering such heterogeneity in the modeling process would better explain commuters’ route choice behaviors, when compared with the traditional MNL model.


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